In real world scenarios of machine learning tasks, classification “errors” may come with diverse meaning incurring significantly different costs; namely, some types of classification errors or so-called misclassifications may be (much) worse than others. For example, rejecting a valid credit card transaction may just cause an inconvenience, while approving a fraud transaction may result in more severe and long lasting consequences. To this end, a classification system may take into account the “cost” of classification error, generally referred to as cost-sensitive classification. There are a number of existing learning algorithms that attempt to deal with cost-sensitive classification, with a relatively limited degree of success. Such algorithms either presume that all types of misclassifications for a given system incur identical losses, or at best attempt to solve the problem by superficially transforming regular classification algorithms to a cost-sensitive version. For example, one can duplicate a particular training example that belongs to a relatively important class (and thus charge more cost when that training example is misclassified), so that the learning model will encounter that training example more times than the less important ones during training. These various learning algorithms are problematic as they do not fundamentally solve the cost-sensitive learning problem and may introduce additional problems, as will be appreciated in light of this disclosure.
Although the following Detailed Description will proceed with reference being made to illustrative embodiments, many alternatives, modifications, and variations thereof will be apparent to those skilled in the art.